text: """
   Copyright (c) 2018 Intel Corporation
  
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
  
        http://www.apache.org/licenses/LICENSE-2.0
  
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
  """
  import numpy as np
  
  from mo.front.common.partial_infer.utils import convert_tf_padding_to_str
  from mo.front.extractor import FrontExtractorOp
  from mo.front.tf.extractors.utils import tf_data_format_spatial, tf_data_format_channel, tf_data_format_batch, \
      tf_int_list
  from mo.ops.pooling import Pooling
  
  
  class AvgPoolFrontExtractor(FrontExtractorOp):
      op = 'AvgPool'
      enabled = True
  
      @staticmethod
      def extract(node):
          attrs = create_pooling_attrs(node, 'avg')
          attrs.update({'op': __class__.op})
          # update the attributes of the node
          Pooling.update_node_stat(node, attrs)
          return __class__.enabled
  
  
  class MaxPoolFrontExtractor(FrontExtractorOp):
      op = 'MaxPool'
      enabled = True
  
      @staticmethod
      def extract(node):
          attrs = create_pooling_attrs(node, 'max')
          attrs.update({'op': __class__.op})
          # update the attributes of the node
          Pooling.update_node_stat(node, attrs)
          return __class__.enabled
  
  
  class MaxPool3DFrontExtractor(FrontExtractorOp):
      op = 'MaxPool3D'
      enabled = True
  
      @staticmethod
      def extract(node):
          attrs = create_pooling_attrs(node, 'max')
          attrs.update({'op': __class__.op})
          # update the attributes of the node
          Pooling.update_node_stat(node, attrs)
          return __class__.enabled
  
  
  def create_pooling_attrs(node, pool_method):
      data_format = node.pb.attr["data_format"]
  
      attrs = {
          'auto_pad': convert_tf_padding_to_str(node.pb.attr['padding']),
          'window': tf_int_list(node.pb.attr["ksize"].list),
          'spatial_dims': tf_data_format_spatial(data_format),
          'pad': None,  # will be inferred when input shape is known
          'stride': tf_int_list(node.pb.attr["strides"].list),
          'pad_spatial_shape': None,
          'output_spatial_shape': None,
          'pool_method': pool_method,
          'type': 'Pooling',
          'layout': data_format.s.decode(),
          'exclude_pad': 'true',
      }
      return attrs